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Cancer remains a leading cause of death worldwide, and many cancers are still diagnosed late, partly due to difficult biomarker discovery and validation across multicancer types. The study contrasts conventional screening methods with blood-based molecular tests, highlights Raman-based SERS advantages, and discusses limitations of earlier biomarker-restricted and data-analysis-limited approaches.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/early-cancer-detection-by-sers-spectroscopy-and-machine-learning-386269/386269/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/early-cancer-detection-by-sers-spectroscopy-and-machine-learning-386269/386269.png","ImageObject",300,407,{"name":92,"@type":93},"Terk","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is early cancer detection important?","Question",{"text":112,"@type":113},"Early detection of cancer or precancerous changes enables earlier intervention and can improve survival. Many cancers are still diagnosed at advanced stages, limiting outcomes.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the approach combine SERS and machine learning?",{"text":117,"@type":113},"It integrates label-free SERS spectroscopy of serum molecular fingerprints with machine learning to classify cancer-related signals. After collecting spectra, dimensionality reduction and a classifier are used for diagnosis.",{"name":119,"@type":110,"acceptedAnswer":120},"What limitations are mentioned for existing SERS-based cancer studies?",{"text":121,"@type":113},"Earlier work is often constrained by using a limited set of biomarkers and by lacking efficient data analysis methods. 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Light: Science & Applications (2023)12:234 Official journal of the CIOMP 2047-7538  \n[https://doi.org/10.1038/s41377-023-01271-7](https://doi.org/10.1038/s41377-023-01271-7) [www.nature.com/lsa](www.nature.com/lsa)  \nNEWS & VIEWS Open Access  \nEarly cancer detection by SERS spectroscopy and machine learning  \nLingyan Shi1 ✉, Yajuan Li1 and Zhi Li1  \nAbstract  \nA new approach for early detection of multiple cancers is presented by integrating SERS spectroscopy of serum molecular fingerprints and machine learning.  \nCancer is a major public health problem and the second leading cause of death worldwide, with 10 million deaths in 20201. Early detection of cancer or precancerous change allows for early intervention and can improve survival rates. However, early detection of many cancers, such as esophageal and ovarian cancers, is still poor, which are often diagnosed at advanced stages2. This is more likely due to multiple remaining challenges, such as finding and validating biomarkers for multicancer types, and technologies applied for cancer detection2.  \nCurrent approaches for cancer screening and detection include blood tests, histopathology tests, imaging tests (for example, mammogram for breast cancer and computerized tomography for lung cancer), and genetic tests. Compared to histopathology and imaging tests, blood tests, which rely on the analysis of specific analytes and tumor biomarkers such as tumor DNA/protein and circulating tumor cells3, are noninvasive and more efficient. However, due to the challenge of validating many potential biomarkers for different cancer types, only a few biomarkers have been validated and used in clinic4.  \nSince the past decade, Raman and infrared spectroscopies have emerged for cancer diagnosis by detecting molecular vibrational fingerprints5. Studies utilized infrared spectroscopy and detected breast, bladder, prostate, and lung cancers via molecular fingerprints in blood plasma and serum6,7. Compared with infrared spectroscopy, various types of Raman spectroscopy are more  \nCorrespondence: Lingyan Shi ([l2shi@ucsd.edu](l2shi@ucsd.edu))  \n1Shu Chien-Gene Lay Department of Bioengineering, UC San Diego, La Jolla, CA 92093, USA  \ncommonly used for cancer detection. Among these, surface-enhanced Raman spectroscopy (SERS) measurement on blood samples is most frequently used in early cancer detection due to its high sensitivity and specificity8–12. Nevertheless, these studies were constrained by their utilization of a very limited set of biomarkers, rendering them unsuitable for detecting multiple types of cancer. Additionally, they lacked efficient data analysis methods. In contrast, the application of machine learning techniques has proven valuable in classifying serum biomarkers to detect cancer and other diseases using omics data13. However, machine learning requires a large independent dataset, while in the previous study only 10 out of a thousand dimensions could be selected for the machine learning algorithm13.  \nIn their article in Light14, Shilian Dong and colleagues presented a method for early cancer detection that uses label-free SERS spectroscopy and machine learning for cancer screening (SERS-AICS) of serum biomarkers. The workflow of SERS-AICS is shown in Fig. 1. The authors analyzed 382 healthy controls and 1582 patients across five cancer types, including lung, colorectal, hepatic, gastric, and esophageal cancers. The SERS-AICS method successfully distinguished cancers from healthy controls with a high overall accuracy (95.81%), sensitivity (95.40%), and specificity (95.87%). Moreover, the method was able to separate precancerous samples from other diseases. This method has important implications for early cancer detection.  \nIn this study, only 15 µl of serum sample each for control and five cancers was used for SERS pan-cancer  \n© The Author(s) 2023  \nOpen AccessThis article is licensed under a Creative Commons Attribution4.0International License, which permits use, sharing, ad","cbCaikK38SXA0RvY","https://ap.wps.com/l/cbCaikK38SXA0RvY","pdf",339118,"English","# Abstract\n## Need for early cancer detection\n## Current screening and detection approaches\n## Raman/infrared spectroscopy and SERS\n## Machine learning and dataset requirements\n## Prior SERS-AICS method and performance metrics\n## Study design overview","[{\"question\":\"Why is early cancer detection important?\",\"answer\":\"Early detection of cancer or precancerous changes enables earlier intervention and can improve survival. Many cancers are still diagnosed at advanced stages, limiting outcomes.\"},{\"question\":\"How does the approach combine SERS and machine learning?\",\"answer\":\"It integrates label-free SERS spectroscopy of serum molecular fingerprints with machine learning to classify cancer-related signals. After collecting spectra, dimensionality reduction and a classifier are used for diagnosis.\"},{\"question\":\"What limitations are mentioned for existing SERS-based cancer studies?\",\"answer\":\"Earlier work is often constrained by using a limited set of biomarkers and by lacking efficient data analysis methods. This makes it less suitable for detecting multiple cancer types.\"}]","Early cancer detection by SERS spectroscopy and machine learning | PDF",1790273037]